






"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A						:			Dict    							=						logging.get_logger(__name__)





class    _UpperCAmelCase			(					__snake_case				):
					SCREAMING_SNAKE_CASE_						:							Dict				       =  ["""pixel_values"""]




					def __init__(							self	:		Optional[int]       ,		A	:		bool = True       ,		A	:		Optional[Dict[str, int]] = None       ,		A	:		PILImageResampling = PILImageResampling.BILINEAR       ,		A	:		bool = True       ,		A	:		Dict[str, int] = None       ,		A	:		bool = True       ,		A	:		Union[int, float] = 1 / 2_55       ,		A	:		bool = True       ,		A	:		Optional[Union[float, List[float]]] = None       ,		A	:		Optional[Union[float, List[float]]] = None       ,		**A	:		str       ,		)    ->    None:
							super().__init__(**__SCREAMING_SNAKE_CASE  )
							lowercase_					:					Optional[int]							=  size if size is not None else {'''shortest_edge''': 2_56}
							lowercase_					:					Optional[int]							=  get_size_dict(__SCREAMING_SNAKE_CASE       ,		default_to_square=__SCREAMING_SNAKE_CASE  )
							lowercase_					:					int							=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_					:					List[str]							=  get_size_dict(__SCREAMING_SNAKE_CASE  )
							lowercase_					:					Any							=  do_resize
							lowercase_					:					List[str]							=  size
							lowercase_					:					List[str]							=  resample
							lowercase_					:					Optional[int]							=  do_center_crop
							lowercase_					:					int							=  crop_size
							lowercase_					:					Any							=  do_rescale
							lowercase_					:					List[Any]							=  rescale_factor
							lowercase_					:					Any							=  do_normalize
							lowercase_					:					Union[str, Any]							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase_					:					int							=  image_std if image_std is not None else IMAGENET_STANDARD_STD




					def     A    (							self	:		Optional[int]       ,		A	:		np.ndarray       ,		A	:		Dict[str, int]       ,		A	:		PILImageResampling = PILImageResampling.BICUBIC       ,		A	:		Optional[Union[str, ChannelDimension]] = None       ,		**A	:		int       ,		)    ->    np.ndarray:
							lowercase_					:					Union[str, Any]							=  get_size_dict(__SCREAMING_SNAKE_CASE       ,		default_to_square=__SCREAMING_SNAKE_CASE  )
							if "shortest_edge" not in size:
									raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
							lowercase_					:					Union[str, Any]							=  get_resize_output_image_size(__SCREAMING_SNAKE_CASE       ,		size=size['''shortest_edge''']       ,		default_to_square=__SCREAMING_SNAKE_CASE  )
							return resize(__SCREAMING_SNAKE_CASE       ,		size=__SCREAMING_SNAKE_CASE       ,		resample=__SCREAMING_SNAKE_CASE       ,		data_format=__SCREAMING_SNAKE_CASE       ,		**__SCREAMING_SNAKE_CASE  )




					def     A    (							self	:		Optional[int]       ,		A	:		np.ndarray       ,		A	:		Dict[str, int]       ,		A	:		Optional[Union[str, ChannelDimension]] = None       ,		**A	:		List[str]       ,		)    ->    np.ndarray:
							lowercase_					:					Optional[Any]							=  get_size_dict(__SCREAMING_SNAKE_CASE  )
							return center_crop(__SCREAMING_SNAKE_CASE       ,		size=(size['''height'''], size['''width'''])       ,		data_format=__SCREAMING_SNAKE_CASE       ,		**__SCREAMING_SNAKE_CASE  )




					def     A    (							self	:		int       ,		A	:		np.ndarray       ,		A	:		float       ,		A	:		Optional[Union[str, ChannelDimension]] = None       ,		**A	:		int  )    ->    np.ndarray:
							return rescale(__SCREAMING_SNAKE_CASE       ,		scale=__SCREAMING_SNAKE_CASE       ,		data_format=__SCREAMING_SNAKE_CASE       ,		**__SCREAMING_SNAKE_CASE  )




					def     A    (							self	:		List[str]       ,		A	:		np.ndarray       ,		A	:		Union[float, List[float]]       ,		A	:		Union[float, List[float]]       ,		A	:		Optional[Union[str, ChannelDimension]] = None       ,		**A	:		Dict       ,		)    ->    np.ndarray:
							return normalize(__SCREAMING_SNAKE_CASE       ,		mean=__SCREAMING_SNAKE_CASE       ,		std=__SCREAMING_SNAKE_CASE       ,		data_format=__SCREAMING_SNAKE_CASE       ,		**__SCREAMING_SNAKE_CASE  )






					def     A    (							self	:		List[str]       ,		A	:		ImageInput       ,		A	:		Optional[bool] = None       ,		A	:		Dict[str, int] = None       ,		A	:		PILImageResampling = None       ,		A	:		bool = None       ,		A	:		Dict[str, int] = None       ,		A	:		Optional[bool] = None       ,		A	:		Optional[float] = None       ,		A	:		Optional[bool] = None       ,		A	:		Optional[Union[float, List[float]]] = None       ,		A	:		Optional[Union[float, List[float]]] = None       ,		A	:		Optional[Union[str, TensorType]] = None       ,		A	:		Union[str, ChannelDimension] = ChannelDimension.FIRST       ,		**A	:		Any       ,		)    ->    List[str]:
							lowercase_					:					Optional[Any]							=  do_resize if do_resize is not None else self.do_resize
							lowercase_					:					int							=  size if size is not None else self.size
							lowercase_					:					Dict							=  get_size_dict(__SCREAMING_SNAKE_CASE       ,		default_to_square=__SCREAMING_SNAKE_CASE  )
							lowercase_					:					Tuple							=  resample if resample is not None else self.resample
							lowercase_					:					Any							=  do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase_					:					int							=  crop_size if crop_size is not None else self.crop_size
							lowercase_					:					List[str]							=  get_size_dict(__SCREAMING_SNAKE_CASE  )
							lowercase_					:					str							=  do_rescale if do_rescale is not None else self.do_rescale
							lowercase_					:					int							=  rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase_					:					Union[str, Any]							=  do_normalize if do_normalize is not None else self.do_normalize
							lowercase_					:					List[str]							=  image_mean if image_mean is not None else self.image_mean
							lowercase_					:					Union[str, Any]							=  image_std if image_std is not None else self.image_std

							lowercase_					:					Tuple							=  make_list_of_images(__SCREAMING_SNAKE_CASE  )

							if not valid_images(__SCREAMING_SNAKE_CASE  ):
									raise ValueError(
									    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
									    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

							if do_resize and size is None:
									raise ValueError('''Size must be specified if do_resize is True.'''  )

							if do_center_crop and crop_size is None:
									raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

							if do_rescale and rescale_factor is None:
									raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

							if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

							# All transformations expect numpy arrays.
							lowercase_					:					Any							=  [to_numpy_array(__SCREAMING_SNAKE_CASE  ) for image in images]

							if do_resize:
									lowercase_					:					Any							=  [self.resize(image=__SCREAMING_SNAKE_CASE       ,		size=__SCREAMING_SNAKE_CASE       ,		resample=__SCREAMING_SNAKE_CASE  ) for image in images]

							if do_center_crop:
									lowercase_					:					Dict							=  [self.center_crop(image=__SCREAMING_SNAKE_CASE       ,		size=__SCREAMING_SNAKE_CASE  ) for image in images]

							if do_rescale:
									lowercase_					:					Tuple							=  [self.rescale(image=__SCREAMING_SNAKE_CASE       ,		scale=__SCREAMING_SNAKE_CASE  ) for image in images]

							if do_normalize:
									lowercase_					:					Dict							=  [self.normalize(image=__SCREAMING_SNAKE_CASE       ,		mean=__SCREAMING_SNAKE_CASE       ,		std=__SCREAMING_SNAKE_CASE  ) for image in images]

							lowercase_					:					str							=  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  ) for image in images]

							lowercase_					:					Any							=  {'''pixel_values''': images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE       ,		tensor_type=__SCREAMING_SNAKE_CASE  )











"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




